Abstract
Continuous glucose monitoring (CGM), a technique that records blood glucose at a regular intervals. While CGM is more commonly used in type 1 diabetes, it is increasingly becoming attractive for treating type 2 diabetic patients. The time series obtained from a CGM provides a rich picture of the glycemic state of the subjects and may help have tighter control on blood sugar by revealing patterns in their physiological responses to food. However, despite its importance, the biophysical understanding of CGM is far from complete. CGM data series is complex not only because it depends on the composition of the food but also varies with individual physiology. All of these make a full modeling of CGM data a difficult task. Here we propose a simple model to explain CGM data in type 2 diabetes. The model combines a relatively simple glucose-insulin dynamics with a two-compartment food model. Using CGM data of a healthy and a diabetic individual we show that this model can capture liquid meals well. The model also allows us to estimate the parameters in a relatively straightforward manner. This opens up the possibility of personalizing the CGM data. The model also predicts insulin time series from the model, and the rate of appearance of glucose due to food. Our methodology thus paves the way for novel analyses of CGM which have not been possible before.
Highlights
Diabetes is a disease in which glucose is the central measure of pathogenesis and diagnosis and its treatment
Continuous glucose monitoring (CGM) has the potential to help millions of people the world over who struggle chronically with obesity and diabetes (Bode, 2000; Klonoff, 2005; Deiss et al, 2006; Murphy et al, 2008; Juvenile Diabetes Research Foundation Continuous Glucose Monitoring Study Group et al, 2009): It holds the promise of utilizing information contained in the time series to gain insight into food
Note that the response to liquid meal at 450 min is fit well while the other two responses to mixed meals are more complex, and these are fit in an “average” sense, in line with expectation
Summary
Diabetes is a disease in which glucose is the central measure of pathogenesis and diagnosis and its treatment. We restrict ourselves largely to CGM data collection; we note that it is plausible to add a few other commonplace measurements including (i) the use of fingerstick glucose measurement, and (ii) a professional (laboratory-based) fasting and postprandial glucose, glycated hemoglobin and insulin measurements carried out a few times while the CGM sensor is implanted. This additional data can help in determining the model, as we describe below. Our methodology appears to be suitable for CGM users widely, with few additional measurements required
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